Apparatus and method for solar energy resource micro-forecasts for solar generation sources and utilities

ABSTRACT

The present invention is an apparatus and method of forecasting solar energy irradiance potential and subsequent photovoltaic output in a region. The apparatus and method includes collecting meteorological data for a given region and then estimating irradiance levels using parameters collected from the meteorological data. Solar energy production is then simulated using the collected meteorological data, estimated irradiance levels, and physical characteristics of a solar generating system in the given region at a predetermined time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of U.S. provisionalapplication No. 61/972,758, entitled “Apparatus and Method for SolarEnergy Resource Micro-Forecasts for Solar Generation Sources andUtilities,” filed on Mar. 31, 2014, the disclosure of which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention include an apparatus and method forforecasting solar energy irradiance potential and subsequentphotovoltaic output in a region.

2. Description of Related Art

Solar energy is becoming increasingly attractive to both retail andcommercial consumers as a means to generate electricity. The maindrawback to solar energy occurs when intermittent cloud cover moves overthe solar field. This is known as solar resource intermittency.Currently, solar intermittency is only a small problem because the ratioof intermittent resources to fossil or nuclear energy sources is small.However, with increasing amounts of solar energy being integrated intoelectricity grids, solar intermittency can become a non-trivial problemfor utilities.

Two methods of mitigating these intermittencies are currently employedby utilities. The first method involves generating electricity fromanother source and feeding that electricity to the area that was beingsupplied by solar. This electricity generation source can come fromlarge scale batteries, fast responding natural gas generators ordiverting electricity from one area to another. Batteries and otherstorage devices are being used to counteract the variation in powerproduction from solar photovoltaic (PV) plants. These storage deviceshelp maintain power quality as well as ensure that variability ofdistributed power generation does not cause unwanted uncertainty inpower demand from the electric utility. A disadvantage of usingbatteries, however, is that they are expensive and susceptible to wearfrom excessive cycling. Calculations have shown that the integratedenergy input/output to a battery system can be reduced by a factor of atleast five if an approximately three minute forecast of PV production isavailable. A second mitigation strategy is to announce price signals andindicators specific to the occlusion event to incentivize a demandreduction, also known as a demand response. In both mitigationstrategies, electricity quantities, either with excess electricity orshortage, have to be actively managed. Common to the success ofelectricity management with interment resources is a need for a forecastof solar energy irradiance potential and subsequent photovoltaic outputpredictions.

SUMMARY

An embodiment of the present invention includes a method of forecastingsolar energy irradiance potential and subsequent photovoltaic output ina region for reducing energy requirements on a utility system. Themethod includes collecting meteorological data for a given region via acamera, estimating irradiance levels using parameters collected from themeteorological data via a neural network coupled to the camera, andforecasting solar energy irradiance potential and subsequentphotovoltaic output in the given region using the collectedmeteorological data, estimated irradiance levels, and physicalcharacteristics of a solar generating system in the given region at apredetermined time in the future via the neural network.

The camera of this embodiment is preferably a shadowband-less fixedcamera using a Si-based sensor or other lower energy photon sensors. Themethod can include analyzing surrounding and occluding clouds as seenfrom one or more pictures from the camera. The camera can take a picturewith lower energy photon wavelengths and is less sensitive to higherenergy photon wavelengths. The method preferably includes simulatingfuture solar energy production of the utility system based on theforecast of solar energy irradiance potential and subsequentphotovoltaic output. The neural network may have a neural networkarchitecture. The network architecture can be a fuzzy artmaparchitecture. The neural network architecture may have weightedconnections associated with each category neuron in a layer that adaptsduring learning. The neural network can include a sub-network andwherein cloud images are processed and presented to the sub-network. Theneural network can also include a second sub-network comprising a solarirradiance signal at a future time. The method can include smoothingphotovoltaic output using a battery system based on the simulated futuresolar energy production and adjusting the energy requirements producedby the utility system based on the simulated future solar energyproduction.

Another embodiment of the present invention includes an apparatus forforecasting solar energy irradiance potential and subsequentphotovoltaic output in a predetermined region for reducing energyrequirements on a utility system. The apparatus can include a camera forcollecting meteorological data for a given region, a neural networkcoupled to the camera for estimating irradiance levels using parameterscollected from the meteorological data. The neural network forecastssolar energy irradiance potential and subsequent photovoltaic output inthe predetermined region using the collected meteorological data,estimated irradiance levels, and physical characteristics of a solargenerating system in the predetermined region at a predetermined time inthe future. The neural network can also simulate future solar energyproduction. The utility system coupled to the neural network can includeenergy requirements that are adjusted based on the simulated futuresolar energy production determined by the neural network. The camera canbe a shadowband-less fixed camera using a Si-based sensor or other lowerenergy photon sensors. The camera can alternatively be a far infraredimaging sensor. The neural network can include a neural architecture.The utility system preferably includes a photovoltaic system. Theutility system can also include one or more batteries coupled to thephotovoltaic system. The neural network can include a sub-network wherecloud images are processed and presented to the sub-network and a secondsub-network having a solar irradiance signal at a future time.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an aerial view of a photovoltaic farm with batterystorage.

FIGS. 2A and 2B are graphs showing solar irradiance signals sampled atabout ten second intervals and smoothed with a trailing window and witha centered window using an about four minute window. FIG. 2A is a4-minute window and FIG. 2B is a 10-minute window.

FIGS. 3A and 3B are graphs showing ramp rate for an example system,including a PV array and battery in FIG. 3A and just a battery in FIG.3B. Note the logarithmic scale on the battery ramp rate distribution.

FIGS. 4A and 4B are graphs illustrating power requirements and energybalance for a smoothing battery using a trailing (solid line) andcentered (dashed line) moving average as a reference signal.

FIG. 5 illustrates a typical near-infrared image of clouds surroundingthe sun.

FIG. 6 illustrates pixel intensity along a line in the direction ofcloud motion passing through the sun for a series of about 20 framesspaced about 10 seconds apart. An opening in the cloud cover approachingthe sun can be seen moving from left to right, coinciding with the sunaround frame 135, position 180. The opening can be seen receding afterthis.

FIG. 7 illustrates a prediction band for the solar irradiance about oneminute after the image is presented to a neural network, shown togetherwith the actual measured value.

FIG. 8 illustrates a diagram of a neural network that associates theoutput from classifier A with the output from classifier B.

FIG. 9 illustrates one input classifier of the neural network.

FIGS. 10A and 10B illustrates how the neural network classifies data atdifferent levels of associativity.

FIG. 11 illustrates a prediction of normalized irradiance with aprediction band representing the high and low predictions within acertain confidence interval.

DETAILED DESCRIPTION

Embodiments of the present invention include an apparatus and method offorecasting solar energy irradiance potential and subsequentphotovoltaic output in a region. The apparatus and method includescollecting meteorological data for a given region and then estimatingirradiance levels using parameters collected from the meteorologicaldata. Solar energy production is then simulated using the collectedmeteorological data, estimated irradiance levels, and physicalcharacteristics of a solar generating system in the given region at apredetermined time. Collecting meteorological data can includecollecting one or more pictures from a camera. Simulating solar energyproduction can include analyzing surrounding and occluding clouds asseen from the one or more pictures from the camera. The camerapreferably takes picture with lower energy photon wavelengths and lesssensitive to higher energy photon wavelengths. In this embodiment, aforecast of solar energy irradiance potential and subsequentphotovoltaic output in the given region is also provided. Acomputational prediction of solar energy irradiance potential andsubsequent photovoltaic output in the given region may also be provided.The computational prediction preferably includes a neural network. Thecomputational prediction can also incorporate a neural networkarchitecture.

An embodiment of the present invention includes an apparatus and methodof using a neural network system to associate a cloud pattern categorywith a future solar irradiance category.

Neural networks are machine-learning systems whose design and processingare inspired by biological nervous systems. These neural networks borrowfrom biology the notions of neurons as the elemental processing unit andthe ways in which neurons are linked via unidirectional adaptiveconnections. Non-limiting examples of neural networks include but arenot limited to feedforward neural networks, kohonen self-organizingnetworks, recurrent or bi-directional networks, radial basis functionnetworks, supervised and unsupervised fuzzy and non-fuzzy maps, andparticle image velocimetry techniques.

A network includes a wiring diagram or graph showing how neurons areinterconnected. Some of the neurons have special roles in the networkacting as interfaces to the environment; for example, receiving inputsfrom sensors or sending signals to motor controllers. The connections ina network have the job of transporting the output of one neuron to theinput of another, and are characterized by their source and targetneurons as well as their connection strength, usually represented as aweight.

The processing of an individual neuron is as follows: First, the neuronintegrates all of its weighted inputs arriving on in-coming connectionsfrom other neurons. Second, it maps this integrated value through apossibly nonlinear function to form a new output. Third, it adapts theconnection strengths on its in-coming connections.

The adaptive process is referred to as “learning” in this field, andfalls into two broad classes: 1) supervised, and 2) self-organizing. Thesupervised method trains data having pairs of input/output samples. Aninput sample is supplied to the network through its input neurons, andthe connection weights are modified to help the output neurons reproducethe output sample. The list of input/output samples is called thetraining set, and one learning pass through this set is called atraining epoch. Through training over many epochs, the output neuronsgradually begin to match the desired output behavior provided by thetraining set.

The second method functions quite differently. In self-organizinglearning, the network is not supplied a desired output for its outputneurons. Instead, during the learning epochs, the weights are modifiedto help the output neurons autonomously encode categories of systematicor regular patterns that exist in the input samples. Often this isreferred to as discovery learning, where the network learns to respondwith a unique output pattern when a member of a category of similarinput patterns is presented to it.

A neural network and its learning methods are referred to as a neuralarchitecture. Neural architectures are typically implemented asalgorithms in computer software simulations. The “goodness” of alearning method is usually quantified by the number of training epochsrequired to reach a given level of output performance.

A preferred embodiment incorporates a neural architecture in the classof self-organizing learning systems. When presented an input pattern, aself-organizing architecture rapidly categorizes it as a member ofeither an existing category or a new (novel) category. If an existingcategory matches the pattern, then the network responds with an existingoutput code indicating its membership in a category. If no existingcategory matches, then the network creates a new output code that willin the future respond to the novel pattern.

Solar Micro-Forecasts for Improving the Efficiency of PV Farm OutputSmoothing

Distribution level photovoltaic (PV) farms are becoming increasinglyattractive for utilities to meet renewable portfolio standards. Theseinstallations, typically with peak power ranging from 0.5 MW to 2 MW,are generally cost-effective, and can be deployed in a matter of months,without lengthy transmission interconnection delays. Siting PV at asingle location provides economies of scale in comparison to residentialPV, and allows a utility to control and maintain the resource moreeffectively. However, because a PV array is within a small geographicarea, it is more susceptible to cloud-driven intermittency than eitherlarge (>100 MW) installations or residential roof mounted installationsof equivalent capacity. Batteries are sometimes deployed to offset powerquality problems due to cloud-driven intermittency. In one non-limitingexample, the deployment of a 500 kW PV farm with 1.0 MWh total smoothingand shifting batteries, located in Albuquerque, N. Mex. was analyzed. Anaerial view of a plant 100 is shown in FIG. 1. Plant 100 is, forexample, a 0.5 MW Prosperity PV plant with battery storage 105. Plant100 occupies approximately four acres (16,000 square meters). Shiftingand smoothing batteries 105 are shown as well as PV panels 110 in FIG.1.

In the example, a 250 kWh subset of a battery system 105, capable ofdelivering up to 500 kW of power, is used to offset cloud-drivenvariability, by delivering power when clouds suddenly occlude the sun,and by absorbing power when the sun re-emerges. The magnitude of thepower delivered or absorbed by the batteries is based on the differencebetween the instantaneous power produced by the PV farm, and anunderlying ‘smooth’ power. The smooth signal is calculated a number ofways including, by using a moving average of the real-time powerproduction, or by a low-pass filter. The size of the moving averagegenerally ranges from about one minute to approximately thirty minutes.The raw signal, and moving averages obtained with a sliding windows ofabout 4-10 minutes are shown in FIGS. 2A and 2B. In this example, solarirradiance signals were sampled at about 10-second intervals (rawirradiance as a solid line in FIGS. 2A and 2B), and smoothed with atrailing window (dashed line with dot in FIGS. 2A and 2B) and with acentered window (dashed line in FIGS. 2A and 2B) using an about 4-minutewindow in FIG. 2A and an about 10-minute window in FIG. 2B.

The moving averages are preferably calculated using a window trailingthe real-time signal, and using a window of the same size, but centeredon the real-time signal. The trailing window signal lags the centeredwindow signal by a time equal to half the size of the window, but isotherwise identical. To ensure that sufficiently smooth power isdelivered to the grid, the power corresponding to the difference betweenthe real-time irradiance and the averaged irradiance is supplied by thebatteries according to:

P _(batt) =P _(smooth) −P _(PV),  (1)

where P_(batt) is the power supplied to the system by the battery,P_(PV) is the power supplied to the system by the PV array, andP_(smooth) is the PV array power averaged over the sliding window.

For simplicity, it is assumed here that the PV array power is directlyproportional to the solar irradiance, although in general PV efficiencyvaries with irradiance and the inverter may clip power when this exceedsthe inverter's own capacity. The 1-second ramp rate frequencydistributions for the system and for the battery is shown in FIGS. 3Aand 3B, for both the 4-minute and 10-minute sliding windows, for asystem of 1 MW capacity with unlimited battery power. The system ramprates for both window sizes have a strong peak centered between ±1 kW/s.The ramp rates for the 10-minute sliding window have secondary peakscontained within ±2 kW/s, while the 4-minute ramp rates have secondarypeaks contained within ±4 kW/s. In the context of a 1 MW system, ramprates of this size can be considered in the same league as noise causedby parasitic loads, such as battery air-conditioning systems, so it canbe concluded that a 10-minute window and a 4-minute window provideequivalent system performance. It is also important to understand theeffect of the window size on the battery itself. This is also shown inFIGS. 3A and 3B. The ramp rate frequency distribution for both windowsizes is essentially equal, meaning that the battery demand is alsolargely unaffected. It is also noteworthy that ramp rates areconcentrated within ±20 kW/s, but there are occasional ramp rates up to100 kW/s in both directions.

Having established that, in terms of system performance and rampingcapacity of the battery, there is little difference between using a4-minute and a 10-minute sliding average. The 4-minute sliding window ispreferred even in the case that a trailing window is used, since the lagtime is 2 minutes instead of 5. Moreover, predicting solar irradiance 2minutes ahead is a more achievable task than 5-minute ahead predictions.

The benefits from using a centered sliding window, rather than atrailing one, are evident from inspection of FIGS. 4A and 4B. Ingeneral, the power that the battery is called on to deliver or absorb issmaller when using a centered window. However, the principal advantagecomes from the amount of energy that the battery must release or store.The maximum deviation of the state of charge from a nominal value isapproximately 8 kWh for the centered window, and 40 kWh for the trailingwindow.

It is evident from inspection of the plots that the difference betweenreal-time irradiance and average irradiance is generally smaller for thecase of the centered sliding window. Thus, by using a centered slidingwindow, the requirement on the batteries is smaller.

While batteries can be designed for this duty cycle, their lifetime isnevertheless a function of the total energy absorbed or delivered. If itwere possible to use a centered window, the lifetime of the smoothingbatteries may be extended substantially. This is illustrated in FIGS. 4Aand 4B. While the power delivered by the batteries using a centered4-minute window is comparable to the power delivered by batteries usinga trailing window, the energy drawn or stored is approximately 100 timessmaller, corresponding to a longer lifetime.

A challenge is that, in the field, only data for past events areavailable, so that only a trailing sliding window can be used. Anembodiment of the present invention provides a short-term prediction forthe future (a micro-forecast), at very low cost, making it possible touse a centered window.

The importance of short-term cloud predictions, as well as means toobtain them, is known. Because of the emphasis on minimal cost, it isassumed that a simple, shadowband-less fixed camera using Si-basedcharge-coupled device (CCD) technology is used to obtain a prediction.Far infrared imaging is also an option, but is expensive. Si-based CCDsare sensitive to near-IR, and a short-pass filter can be placed in frontof the CCD to only allow visible-range photons. When taking imagescentered around the sun, without a shadowband, there is intense glarefrom the sun. The sun's image bleeds through to the pixels around thesun's disk, and in addition, there is atmospheric scattering around thesolar disk, making it very difficult to clearly see clouds that areapproaching the sun. By using a long-pass filter, it is possible toeliminate the visible range entirely from the image projected onto thecamera's CCD, along with some of its undesirable side-effects, includingglare and atmospheric scattering. And clear images of the solar disk andof neighboring clouds are possible. In a non-limiting example, a digitalSLR camera mounted on a tracker (to keep the sun at the center of theimage) was used, combined with a Licor (LI200) pyranometer mounted inthe vicinity. The camera is triggered by an electrical signal providedby a National Instruments/Labview visual instrument, whichsimultaneously obtains a reading from the Licor, so that each image canbe correlated to an irradiance measurement. An example of such an imageis shown in FIG. 5.

If the sun is not occluded, approaching clouds can be distinguished.Similarly, in a field of dark clouds, an opening in the cloud cover canbe seen before it reaches the sun. To illustrate the principle, and inan example, a pixel intensity along a line in the direction of cloudmotion passing through the sun was obtained for a series of imagesspaced ten seconds apart. This is plotted in FIG. 6. The movement of anopening in the cloud cover can clearly be seen, first approaching thesun, then receding. The approaching and receding break in cloud covercan be observed at least 60 seconds before and after its coincidencewith the location of the sun.

Having established the possibility of detecting changes in cloud cover,an embodiment of the present invention includes a tool to interpret theimages and provide a forecast, possibly associated with a measure ofreliability. In keeping with the stated requirement ofcost-effectiveness, the image processing is carried out with relativelysmall processing power, such as might be available, for example, in acamera-equipped ‘smart phone’. To this end, the neural network can beapplied to associate a particular cloud pattern with an irradiance valueabout 60 seconds later. Embodiments of the present invention applies aneural network to an image-based micro-forecast. After training, thecomputational cost of interpreting the input, which can be a large dataset, is minimal, and suitable for deployment on a small portable device.In another non-limiting example, the neural network was trained using asubset of 180 images from a total of 360, and tested using the remainingsubset. Testing is illustrated in FIG. 7. In this example, the networksuccessfully predicted irradiance about 60 seconds in advance of beingexposed to an image. Based on these results, it is possible to obtainpredictions about 120 to about 180 seconds in advance.

In one embodiment of the present invention, a modified consumer-gradeCCD camera is used to collect images of the sky. The camera ispreferably modified so that near-IR light (wavelength less than about1000 nm) makes it to the CCD, while visible light is cut off. The resultis images with better definition of the cloud features, especially closeto the sun. Then, the direction of motion of the clouds is obtained bycross-correlating successive images. The pixel intensity for a stripe inthe direction of cloud motion is obtained across a solar disc. The pixelintensity string, alongside with measured solar intensity data from alight sensor, are used as inputs to the neural network (as discussedabove). Referring to FIG. 8, the neural network associates the outputfrom classifier A with the output from classifier B. In one embodiment,a cloud pattern category is associated with a future solar irradiancecategory. Predictions from real data are accurate up to several minutesahead.

The embodied neural architecture is in a class of self-organizinglearning systems. When presented an input pattern, the network rapidlycategorize the input pattern as a member of either an existing categoryor a new (novel) category. If an existing category matches the pattern,then the network responds with an existing output code indicating itsmembership in a category. If no existing category matches, then thenetwork creates a new output code that in the future responds to thenovel pattern. FIG. 9 shows the input system and internal networkstructure. The input system generically represents the source oftraining data for the system. The input layer is labeled F0 while theoutput layer is labeled F2. For the particular architecture used in thisexample, the output layer is a winner-takes-all structure; the outputcode for a given category of input pattern was represented as a singleactive F2 neuron. As new categories are required, the size of the F2layer grows.

Internal to the network architecture are weighted connections associatedwith each category neuron in the F2 layer that adapt during learning.For a given presentation of an input, only the weights associated withthe active F2 neuron are modified. These weights are symbolicallyrepresented as the triangle marked Tk in FIG. 9 for the winning neuron kin the F2 layer. The weights Tk may be interpreted as a prototype of theinput patterns that activate neuron k. That prototype takes the form ofa hyperbox containing all of the patterns that are members of acategory, as shown in FIGS. 10A and 10B.

With a specific choice of internal configuration, this architecturetends to exhibit single epoch learning convergence. That is, given afinite number of training patterns, the number of learned categories andall internal connection weights converge to their final values in onepresentation epoch. During the second epoch, it is possible thatindividual patterns will change category membership, but this movementwill cease in subsequent epochs. As discussed above, the granularityparameter ρ determines the ultimate number of categories learned duringthe first epoch. When this parameter is unity, the number of categoriesequals the number of unique training patterns, thus memorizing thetraining set. When this parameter is near zero, the number of categoriesapproaches unity, thus over-generalizing on the training set. The choiceof this parameter is therefore strongly application dependent. Asdiscussed above, neural architectures learn to categorize similarpatterns through a process of clustering. For this example, a cloudimage may be represented as a list of pixels: I={i1, i2, . . . , in−1,in}, where n is the number of pixels in the image. Each category isassigned a number and is represented by a learned set of connectionweights referred to as a prototype. As can be seen in FIGS. 10A and 10B,the length of the sides of a box for a prototype is determined by thevariation of the patterns that were clustered together into thatcategory. In other words, the prototype box is sized to contain all ofthe patterns that are members of this cluster, but no larger. For ourapplication, a prototype is composed of n pixel value ranges.

An example network architecture based upon the lateral coupling of twosub-networks, referred to as A and B, is shown in FIG. 8. One example ofa network architecture is a Fuzzy Artmap architecture. Theinterconnections between the two sub-networks force an interaction ofthe respective classifications performed by the sub-networks. Thismodifies their unsupervised learning properties to allow the learning ofinference relationships or associations between the learned patterncategories representing their input domains. This can be thought of assupervised learning, or supervised classification. The input patterns,network layers, and prototype templates are labeled with an A or Breferring to the A or B sub-networks.

In a typical application, a sequence of pairs of input patterns IAk andIBk are presented to sub-networks A and B, respectively. As A and B formcategory prototypes for their inputs, the network learns inferencerelations between their category through a learning process by formingstrong interconnections between pairs of simultaneously-activated F2Aand F2B nodes. Convergence of the network in a finite number of passesthrough a training set requires that it reach the following operationalstate: Presentation of any input pair (IAk, IBk) from the training setresults in pattern IA being assigned a category in sub-network A throughdirect access to its template. Through a strong, learned-inferencingconnection, the active category F2A node signals a unique F2B node towhich it is connected, forcing it to become active. This results in theinferred B category template being read out over the F1B layer just aspattern IB reaches the F1B layer. The ensuing vigilance test insub-network B confirms that the inferred category is an acceptable matchfor IB, forcing the sub-network B vigilance node to remain inactive. Afinal pass through the data results in no resets and no synapticstrength changes (i.e., no learning).

The present approach to learning the relationships between cloudpatterns and their movement at one time, and PV array irradiance at afuture time utilizes a neural network. In the prototype applicationexample, circumsolar cloud images are processed and presented tosub-network A. The solar irradiance signal at a future time is presentedto sub-network B. As the sub-networks form category codes for theseinputs, the network learns inference relations between them within thelateral connection matrix, see FIG. 8. As assessment of the predictiveperformance of the prediction at any given stage of leaning may beproduced by testing it with a validation set of labeled data.

During testing, all learning is disabled. A sequence of pairs of inputpatterns IAk and IBk are drawn from the validation set and presented tosub-networks A and B, respectively. If pattern IAk is assigned acategory in sub-network A through direct access to a prototype and thereexists a lateral connection from its AF2 node to a B sub-network F2node, the inferred B category's template is read out into the BF1 layerin the B sub-network. A prediction includes the inferred B node indexand the associate template hyperbox. A correct inference occurs when theIBk input falls within the inferred hyperbox. An inference error occurswhen the IBk input falls outside of the inferred hyperbox. When an inputarrives at the A sub-network for which there is no direct accessprototype and therefore no inferred B prototype, then an anomaly isdeclared. The suite of performance statistics after passing through thevalidation set includes the following: 1) percentage of correctinferences, 2) percentage of inference errors, 3) percentage ofanomalies, 4) RMS distance of samples outside of hyperboxes wheninference errors occur. In addition, plots the validation IB inputs withthe predicted hyperboxes is produced.

In one example, the input to the neural network was pre-processed bymeasuring pixel intensities along a line through the sun and in thedirection of cloud motion. Thus, each input to the A sub-networkcorresponds to a the pixel intensity trace similar to the ones shown inFIG. 6, while the corresponding input to sub-network B is the irradiancevalue 60 seconds later. The total number of samples is 360. Forcalculating performance measures, the data are equally partitioned intotraining and validation sets. Training required three passes (epochs)through the training set while performance assessment required a singlepass through the validation set. To remove any partitioning bias, thedata were shuffled prior to partitioning. For this example, performancemeasures were averaged over 100 shuffles. Testing is illustrated in FIG.11. It is evident that the network successfully predicts irradiance 60seconds in advance of being exposed to an image. Based on these results,there can be predictions 120 to 180 seconds in advance.

The performance of the predictor is a function of the vigilanceparameters ρ that are applied to each of the sub-networks A and B. Tomeasure the effect of ρ, a number of tests were performed with aconstant value of ρA and various values of ρB, meaning that thegranularity of cloud categories was kept constant, while the granularityof irradiance categories varied.

TABLE 1 Prediction performance as a function of ρB ρ_(B) % ē σ_(ε) % ūσ_(u) Δ S σ_(S) Ε 0.80 4.87 1.89 0.49 0.78 43.7 189.2 88.8 232.9 0.845.20 2.61 0.47 0.78 33.4 160.5 52.7 193.9 0.88 6.61 2.57 0.76 1.02 30.5131.7 19.5 162.2 0.92 8.85 2.92 0.92 1.06 22.6 68.3 34.6 90.9 0.94 10.743.71 1.61 1.14 20.1 49.3 26.9 69.4 0.96 15.24 3.52 2.23 1.70 14.1 29.419.5 43.5 0.98 19.42 4.07 3.94 1.93 7.9 14.2 10.1 22.1

In Table I above, ē/σ_(e) are mean/standard deviation of percentage ofinference errors (an inference error is where the actual irradiancefalls outside the predicted B template interval (1D hyperbox)); ū/σ_(u)are the mean/standard deviation of percent-age on anomalies (an anomalyis where there is no resonant A template for a cloud pattern); Δ is theRMS distance the actual irradiance falls outside the predicted Binterval when an inference error occurs, in units of irradiance; S/σ_(s)are the mean/standard deviation of predicted B template size, again inunits of irradiance; E is the sum of S and σ_(s), and can be consideredas a total prediction error. As ρB increases, the number of irradiancecategories (1-D hyperboxes) increases, so that a better match for thecloud pattern category (360-D hyperboxes) can be found. Correspondingly,the total error decreases. Note that with ρ_(b)=0.98, the predictionerror is only 2% of maximum irradiance.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. The descriptions are not intended to limit the scope of theinvention to the particular forms set forth herein. Thus, the breadthand scope of a preferred embodiment should not be limited by any of theabove-described exemplary embodiments. It should be understood that theabove description is illustrative and not restrictive. To the contrary,the present descriptions are intended to cover such alternatives,modifications, and equivalents as may be included within the spirit andscope of the invention as defined by the appended claims and otherwiseappreciated by one of ordinary skill in the art. The scope of theinvention should, therefore, be determined not with reference to theabove description, but instead should be determined with reference tothe appended claims along with their full scope of equivalents.

What is claimed is:
 1. A method of forecasting solar energy irradiancepotential and subsequent photovoltaic output in a region for reducingenergy requirements on a utility system, the method comprising:collecting meteorological data for a given region via a camera;estimating irradiance levels using parameters collected from themeteorological data via a neural network coupled to the camera; andforecasting solar energy irradiance potential and subsequentphotovoltaic output in the given region using the collectedmeteorological data, estimated irradiance levels, and physicalcharacteristics of a solar generating system in the given region at apredetermined time in the future via the neural network.
 2. The methodof claim 1, wherein the camera is a shadowband-less fixed camera using aSi-based sensor or other lower energy photon sensors.
 3. The method ofclaim 1, further comprising analyzing surrounding and occluding cloudsas seen from one or more pictures from the camera.
 4. The method ofclaim 1, wherein the camera takes a picture with lower energy photonwavelengths and is less sensitive to higher energy photon wavelengths.5. The method of claim 1, further comprising simulating future solarenergy production of the utility system based on the forecast of solarenergy irradiance potential and subsequent photovoltaic output.
 6. Themethod of claim 1 wherein the neural network comprises a neural networkarchitecture.
 7. The method of claim 6, wherein the network architecturecomprise a fuzzy artmap architecture.
 8. The method of claim 6, whereinthe neural network architecture comprises weighted connectionsassociated with each category neuron in a layer that adapts duringlearning.
 9. The method of claim 1 wherein the neural network comprisesa sub-network and wherein cloud images are processed and presented tothe sub-network.
 10. The method of claim 9 wherein the neural networkcomprises a second sub-network comprising a solar irradiance signal at afuture time.
 11. The method of claim 1 further comprising smoothingphotovoltaic output using a battery system based on the simulated futuresolar energy production.
 12. The method of claim 1 further comprisingadjusting the energy requirements produced by the utility system basedon the simulated future solar energy production.
 13. An apparatus forforecasting solar energy irradiance potential and subsequentphotovoltaic output in a predetermined region for reducing energyrequirements on a utility system, the apparatus comprising: a camera forcollecting meteorological data for a given region; a neural networkcoupled to the camera for estimating irradiance levels using parameterscollected from the meteorological data, wherein the neural networkforecasts solar energy irradiance potential and subsequent photovoltaicoutput in the predetermined region using the collected meteorologicaldata, estimated irradiance levels, and physical characteristics of asolar generating system in the predetermined region at a predeterminedtime in the future; wherein the neural network simulates future solarenergy production; and the utility system coupled to the neural network,wherein the energy requirements produced by the utility system areadjusted based on the simulated future solar energy productiondetermined by the neural network.
 14. The apparatus of claim 13 whereinthe camera is a shadowband-less fixed camera using a Si-based sensor orother lower energy photon sensors.
 15. The apparatus of claim 13 whereinthe camera comprises infrared imaging.
 16. The apparatus of claim 13wherein the neural network comprises a neural architecture.
 17. Theapparatus of claim 13 wherein the utility system includes a photovoltaicsystem.
 18. The apparatus of claim 17 wherein the utility systemincludes one or more batteries coupled to the photovoltaic system. 19.The apparatus of claim 13 wherein the neural network comprises asub-network where cloud images are processed and presented to thesub-network.
 20. The apparatus of claim 19 wherein the neural networkcomprises a second sub-network comprising a solar irradiance signal at afuture time.